{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,18]],"date-time":"2026-07-18T16:29:55Z","timestamp":1784392195019,"version":"3.55.0"},"reference-count":71,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T00:00:00Z","timestamp":1692316800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"UAE University"},{"name":"Zayed University"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Informatics"],"abstract":"<jats:p>There are a variety of reasons why smartphones have grown so pervasive in our daily lives. While their benefits are undeniable, Android users must be vigilant against malicious apps. The goal of this study was to develop a broad framework for detecting Android malware using multiple deep learning classifiers; this framework was given the name DroidMDetection. To provide precise, dynamic, Android malware detection and clustering of different families of malware, the framework makes use of unique methodologies built based on deep learning and natural language processing (NLP) techniques. When compared to other similar works, DroidMDetection (1) uses API calls and intents in addition to the common permissions to accomplish broad malware analysis, (2) uses digests of features in which a deep auto-encoder generates to cluster the detected malware samples into malware family groups, and (3) benefits from both methods of feature extraction and selection. Numerous reference datasets were used to conduct in-depth analyses of the framework. DroidMDetection\u2019s detection rate was high, and the created clusters were relatively consistent, no matter the evaluation parameters. DroidMDetection surpasses state-of-the-art solutions MaMaDroid, DroidMalwareDetector, MalDozer, and DroidAPIMiner across all metrics we used to measure their effectiveness.<\/jats:p>","DOI":"10.3390\/informatics10030067","type":"journal-article","created":{"date-parts":[[2023,8,18]],"date-time":"2023-08-18T10:28:48Z","timestamp":1692354528000},"page":"67","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["A Proposed Artificial Intelligence Model for Android-Malware Detection"],"prefix":"10.3390","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-8358-9081","authenticated-orcid":false,"given":"Fatma","family":"Taher","sequence":"first","affiliation":[{"name":"College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Omar","family":"Al Fandi","sequence":"additional","affiliation":[{"name":"College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3180-3861","authenticated-orcid":false,"given":"Mousa","family":"Al Kfairy","sequence":"additional","affiliation":[{"name":"College of Technological Innovation, Zayed University, Dubai 19282, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hussam","family":"Al Hamadi","sequence":"additional","affiliation":[{"name":"College of Engineering and IT, University of Dubai, Dubai 14143, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Saed","family":"Alrabaee","sequence":"additional","affiliation":[{"name":"College of Information Technology, United Arab Emirates University, Al Ain 15551, United Arab Emirates"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,8,18]]},"reference":[{"key":"ref_1","unstructured":"(2023, July 15). Mobile Operating System Market Share Worldwide|Statcounter Global Stats. Available online: https:\/\/gs.statcounter.com\/os-market-share\/mobile\/worldwide."},{"key":"ref_2","unstructured":"(2023, May 02). App Download Data (2022)\u2014Business of Apps. Available online: https:\/\/www.businessofapps.com\/data\/app-statistics\/."},{"key":"ref_3","unstructured":"Shishkova, T. (2021). IT threat evolution in Q2 2021. Mobile statistics. Securelist, 26."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Alzahrani, A.J., and Ghorbani, A.A. (2015, January 21\u201323). Real-time signature-based detection approach for sms botnet. Proceedings of the 2015 13th Annual Conference on Privacy, Security and Trust (PST), Izmir, Turkey.","DOI":"10.1109\/PST.2015.7232968"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Girei, D.A., Shah, M.A., and Shahid, M.B. (2016, January 7\u20138). An enhanced botnet detection technique for mobile devices using log analysis. Proceedings of the 2016 22nd International Conference on Automation and Computing (ICAC), Colchester, UK.","DOI":"10.1109\/IConAC.2016.7604961"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"266","DOI":"10.1016\/j.compeleceng.2017.02.013","article-title":"Machine learning aided Android malware classification","volume":"61","author":"Milosevic","year":"2017","journal-title":"Comput. Electr. Eng."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"S48","DOI":"10.1016\/j.diin.2018.01.007","article-title":"MalDozer: Automatic framework for android malware detection using deep learning","volume":"24","author":"Karbab","year":"2018","journal-title":"Digit. Investig."},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Sihag, V., Vardhan, M., and Singh, P. (2021). A survey of android application and malware hardening. Comput. Sci. Rev., 39.","DOI":"10.1016\/j.cosrev.2021.100365"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"67602","DOI":"10.1109\/ACCESS.2019.2918139","article-title":"Constructing Features for Detecting Android Malicious Applications: Issues, Taxonomy and Directions","volume":"7","author":"Wang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_10","first-page":"4903","article-title":"A novel permission-based Android malware detection system using feature selection based on linear regression","volume":"35","author":"Kural","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_11","unstructured":"Kumars, R., Alazab, M., and Wang, W. (2021). Malware Analysis Using Artificial Intelligence and Deep Learning, Springer."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"8170","DOI":"10.1016\/j.eswa.2010.12.160","article-title":"Empirical study of feature selection methods based on individual feature evaluation for classification problems","volume":"38","author":"Aznarte","year":"2011","journal-title":"Expert Syst. Appl."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"22","DOI":"10.1016\/j.diin.2015.02.001","article-title":"A review on feature selection in mobile malware detection","volume":"13","author":"Feizollah","year":"2015","journal-title":"Digit. Investig."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.cose.2015.02.007","article-title":"Stealth attacks: An extended insight into the obfuscation effects on Android malware","volume":"51","author":"Maiorca","year":"2015","journal-title":"Comput. Secur."},{"key":"ref_15","unstructured":"Chen, Y., and Jiang, X. (2013, January 8\u201310). Droidchameleon: Evaluating android anti-malware against transformation attacks. Proceedings of the 8th ACM SIGSAC Symposium on Information, Computer and Communications Security, Hangzhou, China."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Mariconti, E., Onwuzurike, L., Andriotis, P., De Cristofaro, E., Ross, G., and Stringhini, G. (2016). Mamadroid: Detecting android malware by building markov chains of behavioral models. arXiv.","DOI":"10.14722\/ndss.2017.23353"},{"key":"ref_17","unstructured":"Kabakus, A.T. (2022). Expert Systems with Applications, Elsevier."},{"key":"ref_18","unstructured":"Aafer, Y., Du, W., and Yin, H. (2013). Security and Privacy in Communication Networks: 9th International ICST Conference, SecureComm 2013, Sydney, NSW, Australia, 25\u201328 September 2013, Springer International Publishing."},{"key":"ref_19","unstructured":"Zhu, D., Jin, H., Yang, Y., Wu, D., and Chen, W. (2017, January 3\u20136). DeepFlow: Deep learning-based malware detection by mining Android application for abnormal usage of sensitive data. Proceedings of the IEEE Symposium on Computers and Communications (ISCC), Heraklion, Greece."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Nix, R., and Zhang, J. (2017, January 14\u201319). Classification of Android apps and malware using deep neural networks. Proceedings of the 2017 International Joint Conference on Neural Networks (IJCNN), Anchorage, AK, USA.","DOI":"10.1109\/IJCNN.2017.7966078"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Yang, Y., and Wang, X. (2018, January 16\u201319). A novel android malware detection approach based on convolutional neural network. Proceedings of the 2nd International Conference on Cryptography, Security and Privacy, Guiyang, China.","DOI":"10.1145\/3199478.3199492"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1016\/j.future.2020.02.002","article-title":"Intelligent mobile malware detection using permission requests and API calls","volume":"107","author":"Alazab","year":"2020","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Xu, K., Li, Y., Deng, R.H., and Chen, K. (2018, January 24\u201326). Deeprefiner: Multi-layer android malware detection system applying deep neural networks. Proceedings of the 2018 IEEE European Symposium on Security and Privacy (EuroS&P), London, UK.","DOI":"10.1109\/EuroSP.2018.00040"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Li, W., Wang, Z., Cai, J., and Cheng, S. (2018, January 5\u20138). An android malware detection approach using weight-adjusted deep learning. Proceedings of the 2018 International Conference on Computing, Networking and Communications (ICNC), Maui, HI, USA.","DOI":"10.1109\/ICCNC.2018.8390391"},{"key":"ref_25","unstructured":"(2021, October 19). Statista, Smartphone Users Worldwide 2016\u20132021. Available online: https:\/\/www.statista.com\/statistics\/330695\/number-of-smartphone-users-worldwide\/."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Abuthawabeh, M.K., and Mahmoud, K.W. (2019, January 3\u20135). Android malware detection and categorization based on conversation-level network traffic features. Proceedings of the 2019 International Arab Conference on Information Technology (ACIT), Al Ain, United Arab Emirates.","DOI":"10.1109\/ACIT47987.2019.8991114"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Sihag, V., Vardhan, M., and Singh, P. (2021). BLADE: Robust malware detection against obfuscation in android. Forensic Sci. Int. Digit. Investig., 38.","DOI":"10.1016\/j.fsidi.2021.301176"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"376","DOI":"10.1016\/j.cose.2019.04.005","article-title":"A feature-hybrid malware variants detection using CNN based opcode embedding and BPNN based API embedding","volume":"84","author":"Zhang","year":"2019","journal-title":"Comput. Secur."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"291","DOI":"10.1016\/j.procs.2020.06.034","article-title":"Android malware detection using LSI-based reduced opcode feature vector","volume":"173","author":"Singh","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"345","DOI":"10.1016\/j.procs.2020.06.040","article-title":"Android malware detection based on vulnerable feature aggregation","volume":"173","author":"Roy","year":"2020","journal-title":"Procedia Comput. Sci."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Cai, L., Li, Y., and Xiong, Z. (2021). JOWMDroid: Android malware detection based on feature weighting with joint optimization of weight-mapping and classifier parameters. Comput. Secur., 100.","DOI":"10.1016\/j.cose.2020.102086"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Aboaoja, F.A., Zainal, A., Ghaleb, F.A., and Al-rimy, B.A.S. (2021, January 6\u20137). Toward an Ensemble Behavioral-based Early Evasive Malware Detection Framework. Proceedings of the 2021 International Conference on Data Science and Its Applications (ICoDSA), Bandung, Indonesia.","DOI":"10.1109\/ICoDSA53588.2021.9617489"},{"key":"ref_33","unstructured":"Zhang, H., Qin, J., Zhang, B., Yan, H., Guo, J., and Gao, F. (2020). Security and Privacy in New Computing Environments: Third EAI International Conference, SPNCE 2020, Lyngby, Denmark, 6\u20137 August 2020, Springer Nature."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Frenklach, T., Cohen, D., Shabtai, A., and Puzis, R. (2021). Android malware detection via an app similarity graph. Comput. Secur., 109.","DOI":"10.1016\/j.cose.2021.102386"},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"13271","DOI":"10.1007\/s11042-020-10367-w","article-title":"FSDroid:- A feature selection technique to detect malware from Android using Machine Learning Techniques","volume":"80","author":"Mahindru","year":"2021","journal-title":"Multimedia Tools Appl."},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Hei, Y., Yang, R., Peng, H., Wang, L., Xu, X., Liu, J., Liu, H., Xu, J., and Sun, L. (2021). Hawk: Rapid android malware detection through heterogeneous graph attention networks. IEEE Trans. Neural Networks Learn. Syst., 1\u201315.","DOI":"10.1109\/TNNLS.2021.3105617"},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"5183","DOI":"10.1007\/s00521-020-05309-4","article-title":"MLDroid\u2014Framework for Android malware detection using machine learning techniques","volume":"33","author":"Mahindru","year":"2021","journal-title":"Neural Comput. Appl."},{"key":"ref_38","doi-asserted-by":"crossref","unstructured":"Zhang, X., Wu, K., Chen, Z., and Zhang, C. (2021). MalCaps: A Capsule Network Based Model for the Malware Classification. Processes, 9.","DOI":"10.3390\/pr9060929"},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"de Oliveira, A., and Sassi, R.J. (2020). Chimera: An android malware detection method based on multimodal deep learning and hybrid analysis. TechRxiv.","DOI":"10.21528\/CBIC2021-32"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Kumar, R., Zhang, X., Khan, R.U., and Sharif, A. (2019). Research on Data Mining of Permission-Induced Risk for Android IoT Devices. Appl. Sci., 9.","DOI":"10.3390\/app9020277"},{"key":"ref_41","doi-asserted-by":"crossref","unstructured":"Yadav, P., Menon, N., Ravi, V., Vishvanathan, S., and Pham, T.D. (2022). EfficientNet convolutional neural networks-based Android malware detection. Comput. Secur., 115.","DOI":"10.1016\/j.cose.2022.102622"},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"959","DOI":"10.1016\/j.procs.2021.03.118","article-title":"Towards Explainable CNNs for Android Malware Detection","volume":"184","author":"Kinkead","year":"2021","journal-title":"Procedia Comput. Sci."},{"key":"ref_43","first-page":"23","article-title":"Drebin: Effective and explainable detection of android malware in your pocket","volume":"14","author":"Arp","year":"2014","journal-title":"Ndss"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"307","DOI":"10.1007\/s11416-021-00385-z","article-title":"Android malware classification using convolutional neural network and LSTM","volume":"17","author":"Hosseini","year":"2021","journal-title":"J. Comput. Virol. Hacking Tech."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3313391","article-title":"Mamadroid: Detecting android malware by building Markov chains of behavioral models (extended version)","volume":"22","author":"Onwuzurike","year":"2019","journal-title":"ACM Trans. Priv. Secur."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"844","DOI":"10.1016\/j.future.2020.10.008","article-title":"DeepAMD: Detection and identification of Android malware using high-efficient Deep Artificial Neural Network","volume":"115","author":"SImtiaz","year":"2021","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"39680","DOI":"10.1109\/ACCESS.2021.3063748","article-title":"AdMat: A CNN-on-Matrix Approach to Android Malware Detection and Classification","volume":"9","author":"Vu","year":"2021","journal-title":"IEEE Access"},{"key":"ref_48","unstructured":"Suarez-Tangil, G., and Stringhini, G. (2018). Eight years of rider measurement in the android malware ecosystem: Evolution and lessons learned. arXiv."},{"key":"ref_49","doi-asserted-by":"crossref","unstructured":"Cai, H. (2020, January 13\u201315). Embracing mobile app evolution via continuous ecosystem mining and characterization. Proceedings of the IEEE\/ACM 7th International Conference on Mobile Software Engineering and Systems, Seoul, Republic of Korea.","DOI":"10.1145\/3387905.3388612"},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"127192","DOI":"10.1109\/ACCESS.2022.3226879","article-title":"A Novel Tunicate Swarm Algorithm With Hybrid Deep Learning Enabled Attack Detection for Secure IoT Environment","volume":"10","author":"Taher","year":"2022","journal-title":"IEEE Access"},{"key":"ref_51","unstructured":"(2023, July 22). Apktool\u2014A Tool for Reverse Engineering 3rd Party, Closed, Binary Android Apps. Available online: https:\/\/forum.xda-developers.com\/t\/util-jul-22-2023-apktool-tool-for-reverse-engineering-apk-files.1755243\/."},{"key":"ref_52","doi-asserted-by":"crossref","unstructured":"Felt, A.P., Chin, E., Hanna, S., Song, D., and Wagner, D. (2011, January 17\u201321). Android permissions demystified. Proceedings of the 18th ACM Conference on Computer and Communications Security, Chicago, IL, USA.","DOI":"10.1145\/2046707.2046779"},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1016\/j.tele.2017.01.006","article-title":"Mobile application driven consumer engagement","volume":"34","author":"Tarute","year":"2017","journal-title":"Telemat. Inform."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Hoffmann, J., Ussath, M., Holz, T., and Spreitzenbarth, M. (2013, January 18\u201322). Slicing droids: Program slicing for smali code. Proceedings of the 28th Annual ACM Symposium on Applied Computing, Coimbra, Portugal.","DOI":"10.1145\/2480362.2480706"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"66989","DOI":"10.1109\/ACCESS.2020.2986232","article-title":"Improved Feature Selection Model for Big Data Analytics","volume":"8","author":"Barakat","year":"2020","journal-title":"IEEE Access"},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"119252","DOI":"10.1109\/ACCESS.2020.3005614","article-title":"Optimized ANFIS Model Using Hybrid Metaheuristic Algorithms for Parkinson\u2019s Disease Prediction in IoT Environment","volume":"8","author":"Barakat","year":"2020","journal-title":"IEEE Access"},{"key":"ref_57","unstructured":"Seber, G.A.F., and Lee, A.J. (2012). Linear Regression Analysis, John Wiley & Sons."},{"key":"ref_58","unstructured":"Le, Q., and Mikolov, T. (2014, January 22\u201324). Distributed representations of sentences and documents. Proceedings of the International Conference on Machine Learning, Beijing, China."},{"key":"ref_59","unstructured":"LeCun, Y., and Bengio, Y. (1995). Convolutional networks for images, speech, and time series. Handb. Brain Theory Neural Netw., 3361."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep convolutional neural networks for image classification: A comprehensive review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_62","doi-asserted-by":"crossref","unstructured":"Aldhyani, T.H.H., and Alkahtani, H. (2022). Attacks to Automatous Vehicles: A Deep Learning Algorithm for Cybersecurity. Sensors, 22.","DOI":"10.3390\/s22010360"},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Khan, M.A., Khan, M.A., Jan, S.U., Ahmad, J., Jamal, S.S., Shah, A.A., Pitropakis, N., and Buchanan, W.J. (2021). A Deep Learning-Based Intrusion Detection System for MQTT Enabled IoT. Sensors, 21.","DOI":"10.3390\/s21217016"},{"key":"ref_64","unstructured":"Shi, Q., Petterson, J., Dror, G., Langford, J., Smola, A., Strehl, A., and Vishwanathan, S.V.N. (2009, January 16\u201318). Hash kernels. Proceedings of the 12th International Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater, FL, USA."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"Hinton, G.E., Krizhevsky, A., and Wang, S.D. (2011, January 14\u201317). Transforming auto-encoders. Proceedings of the International Conference on Artificial Neural Networks, Espoo, Finland.","DOI":"10.1007\/978-3-642-21735-7_6"},{"key":"ref_66","first-page":"226","article-title":"A density-based algorithm for discovering clusters in large spatial databases with noise","volume":"96","author":"Ester","year":"1996","journal-title":"KDD"},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Abou-Assaleh, T., Cercone, N., Keselj, V., and Sweidan, R. (2004;, January 28\u201330). N-gram-based detection of new malicious code. Proceedings of the 28th Annual International Computer Software and Applications Conference, Hong Kong, China.","DOI":"10.1109\/CMPSAC.2004.1342667"},{"key":"ref_68","doi-asserted-by":"crossref","unstructured":"Zhou, Y., and Jiang, X. (2012, January 20\u201323). Dissecting android malware: Characterization and evolution. Proceedings of the 2012 IEEE Symposium on Security and Privacy, San Francisco, CA, USA.","DOI":"10.1109\/SP.2012.16"},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Allix, K., Bissyand\u00e9, T.F., Klein, J., and Le Traon, Y. (2016, January 14\u201315). Androzoo: Collecting millions of android apps for the research community. Proceedings of the 13th International Conference on Mining Software Repositories, Austin, TX, USA.","DOI":"10.1145\/2901739.2903508"},{"key":"ref_70","unstructured":"Rosenberg, A., and Hirschberg, J. (2007, January 28\u201330). V-measure: A conditional entropy-based external cluster evaluation measure. Proceedings of the 2007 Joint Conference on Empirical Methods in natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL), Prague, Czech Republic."},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Morcos, M., Gala, M., Al Hamadi, H., Nandyala, S., Mcgillion, B., and Damiani, E. (2022). An ML-Based Recognizer of Exfiltration Attack over Android Platform: MLGuard. TechRxiv.","DOI":"10.36227\/techrxiv.21602706.v1"}],"container-title":["Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/3\/67\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:36:55Z","timestamp":1760128615000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2227-9709\/10\/3\/67"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,8,18]]},"references-count":71,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,9]]}},"alternative-id":["informatics10030067"],"URL":"https:\/\/doi.org\/10.3390\/informatics10030067","relation":{},"ISSN":["2227-9709"],"issn-type":[{"value":"2227-9709","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,8,18]]}}}